{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79363"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79363","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Community Detection in Brain Networks by ARMA Modeling","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Kodukula, Aditya Narasimha Sharma"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Slavakis, Konstantinos","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-04-04T20:30:52Z","date_published":"2019-04-04T20:30:52Z","updated_at":"2026-07-27T19:05:16Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79363","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Slavakis, Konstantinos","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Kodukula, Aditya Narasimha Sharma"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:30:52Z","2019","2018-12-27 13:39:42"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79363"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Community detection in networks is a key research topic in modern network science. Since, human brain can be considered as a complex network system of structurally and functionally linked brain regions, extending community detection techniques to functional brain networks received a great attention in the recent years. Technological advances in noninvasive imaging modalities like functional magnetic resonance imaging (fMRI) helped researchers to analyze the dynamic functional connectivity patterns among different regions of the brain. Recent evidences show that analysis of BOLD (blood-oxygen-level-dependent) fMRI time series, is used to find the functional connectivity patterns in the resting-state-brain- network (RSBN). In this thesis, we use an Autoregressive Moving Average (ARMA) model for extracting features from the fMRI time-series and adopt a key hypothesis from Riemannian Multi-Manifold Modeling to model the functional brain network from the extracted features. Later we apply, community detection algorithms based on Modularity Maximization to detect communities in the brain network."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Community Detection in Brain Networks by ARMA Modeling"]}]}],"canonical_facts":{"dc:contributor":["Slavakis, Konstantinos","Electrical Engineering"],"dc:creator":["Kodukula, Aditya Narasimha Sharma"],"dc:date":["2019-04-04T20:30:52Z","2019","2018-12-27 13:39:42"],"dc:description":["M.S.","Community detection in networks is a key research topic in modern network science. Since, human brain can be considered as a complex network system of structurally and functionally linked brain regions, extending community detection techniques to functional brain networks received a great attention in the recent years. Technological advances in noninvasive imaging modalities like functional magnetic resonance imaging (fMRI) helped researchers to analyze the dynamic functional connectivity patterns among different regions of the brain. Recent evidences show that analysis of BOLD (blood-oxygen-level-dependent) fMRI time series, is used to find the functional connectivity patterns in the resting-state-brain- network (RSBN). In this thesis, we use an Autoregressive Moving Average (ARMA) model for extracting features from the fMRI time-series and adopt a key hypothesis from Riemannian Multi-Manifold Modeling to model the functional brain network from the extracted features. Later we apply, community detection algorithms based on Modularity Maximization to detect communities in the brain network."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79363"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["Community Detection in Brain Networks by ARMA Modeling"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:16Z"}